How will we review machine learning for marketing quality and progress?

We review out-of-sample performance, model stability and operational usefulness against the starting point and agreed criteria. For machine learning for marketing, this also means checking the quality of interpretation and limitations and documenting unresolved dependencies. Reviews explain what has been delivered, what the evidence means and which next steps are justified, rather than treating activity alone as success.